Question

我正在尝试编写一个OpenCV程序，为其他人计算鱼卵。它目前需要上传图像，标准化，模糊，阈值，扩张，距离变换，阈值，然后找到轮廓（如典型的分水岭教程）。

我遇到的问题是光照条件可能会有很大差异，所以即使使用我的自适应阈值，算法的准确性也会有很大差异。如果图像上有渐变亮度，那么它似乎特别差。有时物体在背景下非常明亮，有时它们的光度几乎相同。是否有任何特别有效的方法可以在不同的光照条件下找到物体？

示例图片： gif

Answer 1

因为大于100像素的任何东西与图像无关，我会构建一个傅立叶带通滤波器来移除这些结构。

这是我使用的一个实现，基于ImageJ中的实现。在该实现中，输入图像被镜像填充以减少边缘伪像。

static void GenerateBandFilter(thrust::host_vector<float>& filter, const BandPassSettings& band, const FrameSize& frame)
    {
        //From https://imagej.nih.gov/ij/plugins/fft-filter.html
        if (band.do_band_pass == false)
        {
            return;
        }
        if (frame.width != frame.height)
        {
            throw std::runtime_error("Frame height and width should be the same");
        }
        auto maxN = static_cast<int>(std::max(frame.width, frame.height));//todo make sure they are the same

        auto filterLargeC = 2.0f*band.max_dx / maxN;
        auto filterSmallC = 2.0f*band.min_dx / maxN;
        auto scaleLargeC = filterLargeC*filterLargeC;
        auto scaleSmallC = filterSmallC*filterSmallC;

        auto filterLargeR = 2.0f*band.max_dy / maxN;
        auto filterSmallR = 2.0f*band.min_dy / maxN;
        auto scaleLargeR = filterLargeR*filterLargeR;
        auto scaleSmallR = filterSmallR*filterSmallR;

        // loop over rows
        for (auto j = 1; j < maxN / 2; j++)
        {
            auto row = j * maxN;
            auto backrow = (maxN - j)*maxN;
            auto rowFactLarge = exp(-(j*j) * scaleLargeR);
            auto rowFactSmall = exp(-(j*j) * scaleSmallR);
            // loop over columns
            for (auto col = 1; col < maxN / 2; col++)
            {
                auto backcol = maxN - col;
                auto colFactLarge = exp(-(col*col) * scaleLargeC);
                auto colFactSmall = exp(-(col*col) * scaleSmallC);
                auto factor = (((1 - rowFactLarge*colFactLarge) * rowFactSmall*colFactSmall));
                filter[col + row] *= factor;
                filter[col + backrow] *= factor;
                filter[backcol + row] *= factor;
                filter[backcol + backrow] *= factor;
            }
        }
        auto fixy = [&](float t){return isinf(t) ? 0 : t; };
        auto rowmid = maxN * (maxN / 2);
        auto rowFactLarge = fixy(exp(-(maxN / 2)*(maxN / 2) * scaleLargeR));
        auto rowFactSmall = fixy(exp(-(maxN / 2)*(maxN / 2) *scaleSmallR));
        filter[maxN / 2] *= ((1 - rowFactLarge) * rowFactSmall);
        filter[rowmid] *= ((1 - rowFactLarge) * rowFactSmall);
        filter[maxN / 2 + rowmid] *= ((1 - rowFactLarge*rowFactLarge) * rowFactSmall*rowFactSmall); //
        rowFactLarge = fixy(exp(-(maxN / 2)*(maxN / 2) *scaleLargeR));
        rowFactSmall = fixy(exp(-(maxN / 2)*(maxN / 2) *scaleSmallR));
        for (auto col = 1; col < maxN / 2; col++){
            auto backcol = maxN - col;
            auto colFactLarge = exp(-(col*col) * scaleLargeC);
            auto colFactSmall = exp(-(col*col) * scaleSmallC);
            filter[col] *= ((1 - colFactLarge) * colFactSmall);
            filter[backcol] *= ((1 - colFactLarge) * colFactSmall);
            filter[col + rowmid] *= ((1 - colFactLarge*rowFactLarge) * colFactSmall*rowFactSmall);
            filter[backcol + rowmid] *= ((1 - colFactLarge*rowFactLarge) * colFactSmall*rowFactSmall);
        }
        // loop along column 0 and expanded_width/2
        auto colFactLarge = fixy(exp(-(maxN / 2)*(maxN / 2) * scaleLargeC));
        auto colFactSmall = fixy(exp(-(maxN / 2)*(maxN / 2) * scaleSmallC));
        for (auto j = 1; j < maxN / 2; j++) {
            auto row = j * maxN;
            auto backrow = (maxN - j)*maxN;
            rowFactLarge = exp(-(j*j) * scaleLargeC);
            rowFactSmall = exp(-(j*j) * scaleSmallC);
            filter[row] *= ((1 - rowFactLarge) * rowFactSmall);
            filter[backrow] *= ((1 - rowFactLarge) * rowFactSmall);
            filter[row + maxN / 2] *= ((1 - rowFactLarge*colFactLarge) * rowFactSmall*colFactSmall);
            filter[backrow + maxN / 2] *= ((1 - rowFactLarge*colFactLarge) * rowFactSmall*colFactSmall);
        }
        filter[0] = (band.remove_dc) ? 0 : filter[0];
    }

您可以在我的代码中查看：https://github.com/kandel3/DPM_PhaseRetrieval

Answer 2

计算图像的alpha和beta值 image = cv :: imread（“F：\ Dilated.jpg”）; int x，y; int a = 0; //要在循环中使用的变量 int count = 0; //要在循环中使用的变量

  for( int y = 0; y < image.rows; y++ )
  { for( int x = 0; x < image.cols; x++ )
      { for( int c = 0; c < 3; c++ )
          {
                   image.at<Vec3b>(y,x)[c] =
            saturate_cast<uchar>( alpha*(     image.at<Vec3b>(y,x)[c] ) + beta );
           }
       }
  }

OpenCV中的鲁棒图像分割

2 个答案: